import face_recognition
import cv2
import numpy as np
from PIL import Image, ImageTk, ImageDraw, ImageFont
import urllib.request as request
import requests

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0,cv2.CAP_DSHOW)

# Load a sample picture and learn how to recognize it.
imgurl = 'http://192.168.0.100:8081/face/get_people'
posturl = 'http://192.168.0.100:8081/face/qd'
response = request.urlopen(imgurl)  # 获取系统考勤人员列表
html = response.read()
html = html.decode('utf-8')
#print(html)
str = html #html.split('\n')
print(str)
str = str.split('@')
print(str)
s1 = str[0].split('#')  # 用户名
s2 = str[1].split('#')  # 用户图片url地址
s3 = str[2].split('#')
last_names = []
# Create arrays of known face encodings and their names
known_face_encodings = []
known_face_names = []
i=1
for name, url in zip(s1, s2):
    response = request.urlopen(url)
    img_array = np.array(bytearray(response.read()), dtype=np.uint8)
    img = cv2.imdecode(img_array, -1)
    obama_image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    obama_image = obama_image.convert("RGB")
    obama_image = np.array(obama_image)
    # obama_image = face_recognition.load_image_file("obama.jpg")
    obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

    known_face_encodings.append(obama_face_encoding)
    known_face_names.append(name)
    # Load a second sample picture and learn how to recognize it.
    # biden_image = face_recognition.load_image_file("biden.jpg")
    # biden_face_encoding = face_recognition.face_encodings(biden_image)[0]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()
    ''' 
    i = i + 1
    if i % 100 == 0:
        str_word = '{}{}.jpg'.format("F:/1/", i)
        cv2.imwrite(str_word, frame)
    '''
    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []

        # 将识别到的人脸与图片库进行一一对比
        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"

            # # If a match was found in known_face_encodings, just use the first one.
            # if True in matches:
            #     first_match_index = matches.index(True)
            #     name = known_face_names[first_match_index]

            # Or instead, use the known face with the smallest distance to the new face
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]

            face_names.append(name)

    process_this_frame = not process_this_frame

    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4
        # if name not in last_names:          #判断是否识别到新面孔
        last_names.append(name)
        url = posturl
        body = {"name": name}
        response = requests.post(url, data=body)
        # response = request.urlopen(s)
        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        # cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame = Image.fromarray(frame)

    # PIL图片上打印汉字
    draw = ImageDraw.Draw(frame)  # 图片上打印
    font = ImageFont.truetype("SIMYOU.TTF", 25, encoding="utf-8")  # 参数1：字体文件路径，参数2：

    for (top, right, bottom, left), name in zip(face_locations, face_names):
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4
        width = (right - left) / 4
        draw.text((left + width, bottom - 30), name, (255, 255, 0), font=font)

    frame = cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR)
    # Display the resulting image
    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
